We treat the problem of long range aircraft detection in the presence of evolving cloud clutter. The advantages of a staring infrared camera for this application include passive performance, day and night operation, and rapid frame rate. The latter increases frame correlation of evolving clouds and favors temporal processing. We used targets of opportunity in daytime imagery, which had sub-pixel velocities from 0.1 - 0.5 pixels per frame, to develop and assess two algorithmic approaches. The approaches are: (1) banks of spatio-temporal velocity filters followed by dynamic programming based stage-to-stage association, and (2) a simple recursive temporal filter suggested by a singular value decomposition of the consecutive frame data. In this paper, we outline the algorithms, present representative results in a pictorial fashion, and draw general conclusions on the relative performance. In a second paper, we quantify the relative performance of the two algorithms by applying newly developed metrics to extensive real world data. The temporal filter responds preferentially to pixels influenced by moving point targets over those influenced by drifting clouds and thus achieves impressive cloud clutter suppression without requiring sub-pixel frame registration. It is roughly twice as effective in clutter suppression when results are limited by cloud evolution. However when results are limited by temporal noise (close to blue sky conditions), the velocity filter approach is roughly twice as sensitive to weak targets in our velocity range. Real-time hardware implementation of the temporal filter is far more practical and is underway.